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    "colab": {
      "name": "time_series.ipynb",
      "provenance": []
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rOoC1NVjRcT",
        "colab_type": "text"
      },
      "source": [
        "# Simple Time Series Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSEcFiPijRcb",
        "colab_type": "text"
      },
      "source": [
        "This tutorial shows how a simple time series simulation is performed with the timeseries and control module in pandapower. A time series calculation requires the minimum following inputs:\n",
        "* pandapower net\n",
        "* the time series (in a pandas Dataframe for example)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YquzqXkKjRcd",
        "colab_type": "text"
      },
      "source": [
        "First we need some imports. Specific for this example are:\n",
        "* ConstControl -> \"constant\" controllers, which change the P and Q values of sgens and loads\n",
        "* DFData -> The Dataframe Datasource. This Dataframe holds the time series to be calculated\n",
        "* OutputWriter -> The output writer, which is required to write the outputs to the hard disk\n",
        "* run_timeseries -> the \"main\" time series function, which basically calls the controller functions (to update the P, Q of the ConstControllers) and runpp."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dx9ZKvbfjRcg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tempfile\n",
        "\n",
        "import pandapower as pp\n",
        "from pandapower.timeseries import DFData\n",
        "from pandapower.timeseries import OutputWriter\n",
        "from pandapower.timeseries.run_time_series import run_timeseries\n",
        "from pandapower.control import ConstControl"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJj7q_srjRcs",
        "colab_type": "text"
      },
      "source": [
        "First we look at the time series example function. It follows these steps:\n",
        "1. create a simple test net\n",
        "2. create the datasource (which contains the time series P values)\n",
        "3. create the controllers to update the P values of the load and the sgen\n",
        "4. define the output writer and desired variables to be saved\n",
        "5. call the main time series function to calculate the desired results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5i2ezcDjRcw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def timeseries_example(output_dir):\n",
        "    # 1. create test net\n",
        "    net = simple_test_net()\n",
        "\n",
        "    # 2. create (random) data source\n",
        "    n_timesteps = 24\n",
        "    profiles, ds = create_data_source(n_timesteps)\n",
        "    # 3. create controllers (to control P values of the load and the sgen)\n",
        "    create_controllers(net, ds)\n",
        "\n",
        "    # time steps to be calculated. Could also be a list with non-consecutive time steps\n",
        "    time_steps = range(0, n_timesteps)\n",
        "\n",
        "    # 4. the output writer with the desired results to be stored to files.\n",
        "    ow = create_output_writer(net, time_steps, output_dir=output_dir)\n",
        "\n",
        "    # 5. the main time series function\n",
        "    run_timeseries(net, time_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhzr8EE5jRc3",
        "colab_type": "text"
      },
      "source": [
        "We start by creating a simple example pandapower net consisting of five buses, a transformer, three lines, a load and a sgen. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8hpgqhQjRc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def simple_test_net():\n",
        "    \"\"\"\n",
        "    simple net that looks like:\n",
        "\n",
        "    ext_grid b0---b1 trafo(110/20) b2----b3 load\n",
        "                                    |\n",
        "                                    |\n",
        "                                    b4 sgen\n",
        "    \"\"\"\n",
        "    net = pp.create_empty_network()\n",
        "    pp.set_user_pf_options(net, init_vm_pu = \"flat\", init_va_degree = \"dc\", calculate_voltage_angles=True)\n",
        "\n",
        "    b0 = pp.create_bus(net, 110)\n",
        "    b1 = pp.create_bus(net, 110)\n",
        "    b2 = pp.create_bus(net, 20)\n",
        "    b3 = pp.create_bus(net, 20)\n",
        "    b4 = pp.create_bus(net, 20)\n",
        "\n",
        "    pp.create_ext_grid(net, b0)\n",
        "    pp.create_line(net, b0, b1, 10, \"149-AL1/24-ST1A 110.0\")\n",
        "    pp.create_transformer(net, b1, b2, \"25 MVA 110/20 kV\", name='tr1')\n",
        "    pp.create_line(net, b2, b3, 10, \"184-AL1/30-ST1A 20.0\")\n",
        "    pp.create_line(net, b2, b4, 10, \"184-AL1/30-ST1A 20.0\")\n",
        "\n",
        "    pp.create_load(net, b2, p_mw=20., q_mvar=10., name='load1')\n",
        "    pp.create_sgen(net, b4, p_mw=20., q_mvar=0.15, name='sgen1')\n",
        "\n",
        "    return net"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CcyC2mgjRc_",
        "colab_type": "text"
      },
      "source": [
        "The data source is a simple pandas DataFrame. It contains random values for the load and the sgen P values (\"profiles\"). Of course your time series values should be loaded from a file later on.\n",
        "Note that the profiles are identified by their column name (\"load1_p\", \"sgen1_p\"). You can choose here whatever you prefer.\n",
        "The DFData(profiles) converts the Dataframe to the required format for the controllers. Note that the controller"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVrcajmqjRdB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_data_source(n_timesteps=24):\n",
        "    profiles = pd.DataFrame()\n",
        "    profiles['load1_p'] = np.random.random(n_timesteps) * 20.\n",
        "    profiles['sgen1_p'] = np.random.random(n_timesteps) * 20.\n",
        "\n",
        "    ds = DFData(profiles)\n",
        "\n",
        "    return profiles, ds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_29FDhugjRdJ",
        "colab_type": "text"
      },
      "source": [
        "create the controllers by telling the function which element_index belongs to which profile. In this case we map:\n",
        "* first load in dataframe (element_index=[0]) to the profile_name \"load1_p\"\n",
        "* first sgen in dataframe (element_index=[0]) to the profile_name \"sgen1_p\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7jNYZYhjRdN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_controllers(net, ds):\n",
        "    ConstControl(net, element='load', variable='p_mw', element_index=[0],\n",
        "                 data_source=ds, profile_name=[\"load1_p\"])\n",
        "    ConstControl(net, element='sgen', variable='p_mw', element_index=[0],\n",
        "                 data_source=ds, profile_name=[\"sgen1_p\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDA6xHDWjRdT",
        "colab_type": "text"
      },
      "source": [
        "create the output writer. Instead of saving the whole net (which takes a lot of time), we extract only pre defined outputs.\n",
        "In this case we:\n",
        "* save the results to \"../timeseries/tests/outputs\" \n",
        "* write the results to \".xls\" Excel files. (Possible are: .json, .p, .csv)\n",
        "* log the variables \"p_mw\" from \"res_load\", \"vm_pu\" from \"res_bus\" and two res_line values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlttGzEHjRdV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_output_writer(net, time_steps, output_dir):\n",
        "    ow = OutputWriter(net, time_steps, output_path=output_dir, output_file_type=\".xls\", log_variables=list())\n",
        "    # these variables are saved to the harddisk after / during the time series loop\n",
        "    ow.log_variable('res_load', 'p_mw')\n",
        "    ow.log_variable('res_bus', 'vm_pu')\n",
        "    ow.log_variable('res_line', 'loading_percent')\n",
        "    ow.log_variable('res_line', 'i_ka')\n",
        "    return ow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAIvq_bUjRdb",
        "colab_type": "text"
      },
      "source": [
        "Now lets execute the code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTlSALR0jRdd",
        "colab_type": "code",
        "colab": {},
        "outputId": "959014ba-8db7-412b-d425-f2af199207f0"
      },
      "source": [
        "output_dir = os.path.join(tempfile.gettempdir(), \"time_series_example\")\n",
        "print(\"Results can be found in your local temp folder: {}\".format(output_dir))\n",
        "if not os.path.exists(output_dir):\n",
        "    os.mkdir(output_dir)\n",
        "timeseries_example(output_dir)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results can be found in your local temp folder: /tmp/time_series_example\n",
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8-ZYJFujRdk",
        "colab_type": "text"
      },
      "source": [
        "If everything works you should have the desired results the output_folder, which is the temporary folder of your operating system (see print statement above)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzTC_NnRjRdn",
        "colab_type": "text"
      },
      "source": [
        "## Plot the result\n",
        "Let's read the result from the disk and plot it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhVd7-3ejRdp",
        "colab_type": "code",
        "colab": {},
        "outputId": "101d40f1-78fc-485d-eac9-70b90baf3abf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  \n",
        "\n",
        "# voltage results\n",
        "vm_pu_file = os.path.join(output_dir, \"res_bus\", \"vm_pu.xls\")\n",
        "vm_pu = pd.read_excel(vm_pu_file)\n",
        "vm_pu.plot(label=\"vm_pu\")\n",
        "plt.xlabel(\"time step\")\n",
        "plt.ylabel(\"voltage mag. [p.u.]\")\n",
        "plt.title(\"Voltage Magnitude\")\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# line loading results\n",
        "ll_file = os.path.join(output_dir, \"res_line\", \"loading_percent.xls\")\n",
        "line_loading = pd.read_excel(ll_file)\n",
        "line_loading.plot(label=\"line_loading\")\n",
        "plt.xlabel(\"time step\")\n",
        "plt.ylabel(\"line loading [%]\")\n",
        "plt.title(\"Line Loading\")\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# load results\n",
        "load_file = os.path.join(output_dir, \"res_load\", \"p_mw.xls\")\n",
        "load = pd.read_excel(load_file)\n",
        "load.plot(label=\"load\")\n",
        "plt.xlabel(\"time step\")\n",
        "plt.ylabel(\"P [MW]\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjqeC1Z3jRdv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkhFcVfYjRdz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
